COBRA: Cell-type-specific Orthogonal Batch effect Removal Algorithm in single cell RNA-sequencing data

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ID: 326931
2026
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Abstract
MOTIVATION: Single-cell RNA sequencing (scRNA-seq) enables high-resolution profiling of cellular heterogeneity, yet batch effects remain a critical challenge in data integration. Existing batch correction methods often assume homogeneous batch effect across cell types, operate in reduced-dimensional space leading to potential loss of biological information, and require extensive computational resources. RESULTS: Here, we introduce COBRA, a linear model-based batch correction method that explicitly adjusts cell-type-specific batch effect. By orthogonalizing batch-associated parameters with respect to biological variables, COBRA removes technical artifacts while preserving biologically meaningful transcriptional differences. When cell type annotations are unavailable, COBRA implements an iterative clustering algorithm to estimate pseudo-cell types while accounting for batch effects. COBRA retains the full gene expression matrix, ensuring seamless integration for downstream analyses. We evaluated COBRA across simulated and real-world datasets, including type 2 diabetes and COVID-19 datasets. COBRA outperformed in terms of batch mixing efficiency, preservation of biological group structure, and accuracy of differentially expressed gene detection. AVAILABILITY: COBRA is freely available at https://github.com/wonlab-healthstat/COBRA. The code to reproduce the analyses is archived at Zenodo (https://doi.org/10.5281/zenodo.19891355). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7205005574 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sujin Seo, Sungho Won, Kyungtaek Park
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag660
URL
https://doi.org/10.1093/bioinformatics/btag660
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